Biofertilizer outcompete chemical fertilizer in enhancing carbon sequestration in Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau) forests
Bibliographic record
Abstract
Biofertilizers present an efficient alternative to chemical fertilizers, yet how they, together with native microbiome, affect carbon (C) sequestration and bamboo product yield in forest ecosystems remain unclear. This study investigated the differential impacts of chemical fertilizer and biofertilizer on C sequestration of Moso bamboo ( Phyllostachys edulis (Carriere) J. Houzeau) ecosystem scale by examining vegetation C storage, soil organic C (SOC) pool, soil microbial community, and greenhouse gas emissions. The results revealed that both fertilizers increased vegetation C storage and SOC pool in the top soil (0–40 cm), with biofertilizer showing more pronounced effects than chemical fertilizer. Particularly, biofertilizer significantly increased the fungal-to-bacterial residue carbon ratio, indicating a shift toward fungal dominance rather than a sole increase in fungal residue carbon (FRC). While chemical fertilizer increased soil CO 2 and N 2 O emissions, biofertilizer significantly reduced N 2 O emissions and enhanced Moso bamboo forest C sequestration capacity compared to the control. Native microbiome responses to fertilizations showed that biofertilizer primarily influenced the taxonomic structure of the fungal community, while inducing notable functional changes within the bacterial community. These findings suggest that biofertilizers are more effective than chemical fertilizers in optimizing bamboo forest management and enhancing C sequestration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".